Yes, the cause is memory use patterns, but the price is steep nonetheless.
E.g.:
rate<-log(400*1.1^(1:30)) # runs about 27x times as fast as the following
(test via 'microbenchmark')
rate<-numeric(30)
for (i in 1:30){
rate[i]<-log(400*1.1^i)
}
When manipulating large arrays, the difference can easily be a few seconds
vs. an hour or more. And if many such arrays need to be run, the difference
is between "difficult" and "not feasible".
-Dan
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